PH Deck

Currently, users face challenges in integrating various AI models into their applications. The old solution involves manually integrating these models, which can be time-consuming and complex. A significant drawback of this old situation is the need to manually manage and integrate multiple AI models, leading to increased costs and possible integration errors.

Users face challenges in integrating AI capabilities into their platforms like OpenAI and Azure OpenAI

Developers face challenges in accessing affordable, efficient, and stable AI music APIs that integrate multiple music providers.

Old solution: Limited options and high costs for accessing AI music APIs, leading to inefficiencies and higher expenses.

Users struggle with inefficient manual data transfer between different applications

Users face challenges in communication during meetings due to the lack of efficient note-taking, real-time assistance, and seamless integration of communication tools.

Lack of efficient note-taking, real-time assistance, and seamless integration of communication tools.

Users previously managed e-commerce integrations manually across multiple vendors and systems, facing inefficient real-time product discovery, scalability challenges, and fragmented checkout processes.

Developers require access to various AI models for innovation but face the issue of integrating multiple APIs, leading to increased complexity and higher costs. The integration of multiple APIs and the associated high costs are significant drawbacks.

Users struggle to utilize AI models, integrate knowledge bases, and simplify tasks without expertise

With older solutions for API and agent integration, users often face complexities in manually discovering, testing, and integrating APIs. These processes are often inefficient and time-consuming, especially when handling large volumes of APIs or complex integrations. Additionally, maintaining updated lists of verified APIs and accessing the latest tools and Large Language Models (LLMs) pose substantial challenges.

Users need to integrate multiple AI models into their applications, each requiring separate APIs, which leads to difficult management and inconsistent request/response formats. This complexity results in increased development time and maintenance overhead.